| Variable | N | Mean | SD | P0 | P25 | P50 | P75 | P100 |
|---|---|---|---|---|---|---|---|---|
| x1 | 11 | 9.0 | 3.32 | 4.00 | 6.50 | 9.00 | 11.50 | 14.00 |
| y1 | 11 | 7.5 | 2.03 | 4.26 | 6.31 | 7.58 | 8.57 | 10.84 |
| x2 | 11 | 9.0 | 3.32 | 4.00 | 6.50 | 9.00 | 11.50 | 14.00 |
| y2 | 11 | 7.5 | 2.03 | 3.10 | 6.70 | 8.14 | 8.95 | 9.26 |
| x3 | 11 | 9.0 | 3.32 | 4.00 | 6.50 | 9.00 | 11.50 | 14.00 |
| y3 | 11 | 7.5 | 2.03 | 5.39 | 6.25 | 7.11 | 7.98 | 12.74 |
| x4 | 11 | 9.0 | 3.32 | 8.00 | 8.00 | 8.00 | 8.00 | 19.00 |
| y4 | 11 | 7.5 | 2.03 | 5.25 | 6.17 | 7.04 | 8.19 | 12.50 |
Data Exploration and Visualization
Data Analytics for Finance
Caspar David Peter 
Rotterdam School of Management, Accounting Department
Data Exploration and Visualization
Recap - Where did we leave things?
Last time: The Research Pipeline
Raw & Clean Data
- Data acquisition (WRDS, identifiers)
- Merging datasets (CUSIP, GVKEY, PERMNO)
- Understanding and creating tidy data
Today’s Menu
- Data exploration & visualization
- Descriptive statistics
- Effective communication of results
Today we learn how to LOOK at what we’ve built
Why Visualize? The Power and Peril
Visualization in Practice
Just Eat Takeaway Analyst Presentation
What do you notice?
Key observation
Almost every slide contains a visualization!
Key question
Why do they do this?
Visualization in Practice
Growth Story
Gross Trsansaction Value Over Time
What story does this tell?
- “~2X growth since pre-pandemic”
- Clear trend, immediate impact
- Visual > table of numbers
Visualization in Practice
Geographic Breakdown
Market Leadership Across Regions
Multiple dimensions in one view
- Geography
- Market position
- Revenue by segment
Visualization in Practice
Comparative Performance
EBITDA by region over time
What does this show?
- Multiple entities (regions)
- Time dimension
- Positive vs. negative values
- Clear color coding
Why Visualize? The Power and Peril
But… Visualization Can Mislead
Why Visualize? The Power and Peril
But… Visualization Can Mislead
When keepin’ it vis goes wrong
OpenAI
Anthropic
Why Visualize? The Power and Peril
But… Visualization Can Mislead
When keepin’ it vis goes wrong
Pizza toppings in the UK
Female height distribution
Why Visualize? The Power and Peril
Distortion Example Overview
Why Visualize? The Power and Peril
Anscombe’s Quartet - The Famous Example
Identical Statistics, Different Stories
Anscombe’s Quartet Data
Visuals Matter
Describing Variables
Types of Variables
What according to Munzner (2025)
What we typically deal with…
- Continuous: Price, returns, income
- Count: Number of mergers, trades
- Ordinal: Credit ratings (AAA, AA, A…)
- Categorical: Industry, country
- Binary: Default/no default
Describing Variables
Understanding Distributions
Common Distribution Shapes
What is a distribution?
- The pattern of values a variable can take
- Key question: How is the data spread out?
- Examples:
- Are most returns clustered around zero?
- Do some firms have extremely high market caps?
- Is income evenly distributed or skewed?
Describing Variables
Visualizing Distributions - Continuous Variables
Histograms
The Classic Choice
- Divides data into bins
- Height → frequency in each bin
Describing Variables
Visualizing Distributions - Continuous Variables
Histograms
The Classic Choice
- Divides data into bins
- Height → frequency in each bin
- Design choices matter!:
- Bin width too narrow → noise
- Bin width too wide → oversmoothing
Describing Variables
Visualizing Distributions - Density Plots
The Smooth Alternative
- Continuous curve estimate
- No arbitrary bin choices
- Easier to compare multiple distributions
Describing Variables
Visualizing Distributions - Density Plots
The Smooth Alternative
- Continuous curve estimate
- No arbitrary bin choices
- Easier to compare multiple distributions
Describing Variables
The Numbers Behind the Picture
Summary Statistics
Location (central tendency)
- Mean: Average value
- Median: 50th percentile
- Mode: Most frequent value
Spread (variability):
- Standard deviation: Average distance from mean
- Variance: SD squared
- Range: Max - Min
- Interquartile range (IQR): Q3 - Q1
Percentile
→ value below which X% of observations fall
Examples:
- 25th percentile (Q1): Bottom quarter
- 50th percentile (Median): Middle
- 75th percentile (Q3): Top quarter
- 1st/99th percentile: Extreme values (outliers?)
Putting it all together (Box Plot)
Describing Variables
Describing Relationships Between Variables
The Scatter Plot
Two Continuous Variables
What to look for
- Direction (positive/negative)
- Strength (tight cluster vs. diffuse)
- Linearity (straight line or curve?)
- Outliers
Describing Variables
Describing Relationships Between Variables
Correlation vs. Causation
Correlation vs. Causation
The Most Important Distinction in Statistics
- Correlation: X and Y move together
- Causation: X causes Y
Describing Variables
Describing Relationships Between Variables
Time Series Plots
Daily Study Hours Over Time”
Basics of Time Series Plots
- X-axis: Time
- Y-axis: Variable of interest
- Connect the dots in chronological order
Essential for …
- Event studies (Lecture 5)
- (Parallel) Trend analysis (Lecture 4)
- Structural breaks
- Seasonality
Describing Variables
Describing Relationships Between Variables
Multiple Time Series - Comparison
VW vs. Competitors - TEASER A2
What this reveals
- Contagion effects
- Relative performance
- Industry-wide vs. firm-specific shocks
Design Principles That Matter
Munzner (2025)’s Visualization Framework (Simplified)
Munzner (2025)
Three Important Questions
What? - What data do I have?
- Tables, networks, time series, spatial data
- We mostly work with: Tables (panel data)
Design Principles That Matter
Munzner (2025)’s Visualization Framework (Simplified)
Munzner (2025)
Three Important Questions
What? - What data do I have?
- Tables, networks, time series, spatial data
- We mostly work with: Tables (panel data)
Why? - Why am I visualizing?
- Communication, exploration, verification
Sesign Principles That Matter
Munzner (2025)’s Visualization Framework (Simplified)
Munzner (2025)
Three Important Questions
What? - What data do I have?
- Tables, networks, time series, spatial data
- We mostly work with: Tables (panel data)
Why?- Why am I visualizing?
- Communication, exploration, verification
How? - How should I encode the data?
- Position, color, size, shape
Design Principles That Matter
The Data-Ink Ratio
Edward Tufte’s Core Principle
\[ \text{Data-ink ratio} = \frac{\text{ink used to display data}}{\text{total ink used}}\]
Goal: Maximize this ratio
- Remove chart junk
- Eliminate non-data ink
- Let the data speak
Example
Design Principles That Matter
Accessibility - Colorblind Considerations
~8% of men have color vision deficiency
Avoid red-green combinations
Use colorblind-safe palettes: Viridis
Design Principles That Matter
The Iteration Process
Tables
Table Design Principles
Example & Teaser for Assignment 6
Huck (2024), Table 2
Appendix
Plot types overview
Proportions
Single Distribution
Appendix
Plot types overview
Multiple Distributions
Appendix
Plot types overview
Proportions - 2 + Groups
